Intrapatient Tacrolimus Variability is Associated with Medical Nonadherence among Pediatric Kidney Transplant Recipients

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Gavcovich, Marissa J. DeFreitas, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5334772/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Mar, 2025 Read the published version in Frontiers in Transplantation → Version 1 posted You are reading this latest preprint version Abstract Background: Long-term survival of kidney allografts is limited by multiple factors, including nonadherence. High intrapatient variability (IPV) in tacrolimus levels (≥30%) is associated with de novo donor-specific antibody ( dn DSA) formation, increased risk of rejection and graft loss. Methods: We prospectively analyzed the association between tacrolimus IPV and nonadherence in pediatric kidney transplant recipients. We derived a composite adherence score from 0-3 points based on (1) Basel Assessment of Adherence to Immunosuppressive Medical Scale Ó ; (2) healthcare team score; and (3) intentionally missed laboratory or clinic visits. A score of 1 or more was considered nonadherent. Tacrolimus 12-hour trough levels, patient characteristics and clinical outcomes were collected. Tacrolimus IPV was calculated as the coefficient of variation. Results: The nonadherent group had a significantly higher median tacrolimus IPV (31%) as compared to the adherent cohort (20%) (p < 0.001), with a positive correlation between tacrolimus IPV and composite adherence score (r = 0.44, p < 0.001). Antibody and T-cell mediated rejection, along with dn DSA formation were more prevalent in nonadherent patients and those with a high tacrolimus IPV. Conclusions: Tacrolimus IPV may be a useful biomarker to identify high-risk patients, allowing for early interventions to prevent adverse graft outcomes. kidney transplant pediatric adherence tacrolimus variability Figures Figure 1 Figure 2 1. Introduction Allograft rejection due to insufficient immunosuppression is a major contributor to early graft loss 1 – 3 . Not taking medications consistently or inconsistent follow-up with the medical care team is a known risk factor for poor graft outcomes, with nonadherence being the most common in adolescent patients 1 , 4 , 5 . Post-transplant maintenance immunosuppression typically includes tacrolimus, a calcineurin inhibitor, with a narrow therapeutic index requiring frequent drug level monitoring to balance effective drug concentrations while minimizing toxicity 6 – 8 . High tacrolimus intrapatient variability (Tac IPV) has been studied for over two decades and measured with two different methods, the coefficient of variation (CV) and Medication Level Variability Index (MLVI). Tac IPV has been increasingly recognized as a biomarker for graft rejection and loss 7 , 9 – 12 and a marker of nonadherence 13 . The Medication Adherence in children who had a Liver Transplant (MALT) prospective multi-site study evaluated whether MLVI predicts late acute rejection. A total of 379 participants were followed prospectively and results showed that a higher prerejection MLVI predicted adverse graft outcomes 14 . The International Consensus on Managing Modifiable Risk in Transplantation recommends monitoring nonadherence as a fifth vital sign 15 . As expected, nonadherence has also been associated with worse graft outcomes, leading to increased incidence of rejection, de novo donor specific antibody ( dn DSA) formation, decreased renal function and ultimately graft loss 1 , 16 , 17 . While multiple factors contribute to raising the Tac IPV, including tube feeding, feeding intolerance, infection, drug or food interactions and dose adjustments 6 , 8 , 12 , 18 – 21 , medication nonadherence is thought to be the strongest contributor 22 . Several studies have shown that tacrolimus variability responds to behavioral interventions strongly supporting that is affected by behavior 23 – 26 . This relationship, however, has not been described well in adults or children, especially since nonadherence is difficult to measure consistently in the clinical setting 27 , 28 . The prevalence of nonadherence varies across studies, and depends on heterogenous measurement tools, which adds to the inconsistent analyses 29 . Measures of nonadherence include direct (observation, drug assays) and indirect (self-report, collateral report, prescription refills, electronic monitoring) parameters, and there is no single ideal method given the limitations of each 27 , 29 – 31 . Since adherence measures can be quite unreliable and burdensome to patients and clinicians, tacrolimus variability has been suggested as a potential objective biomarker for nonadherence, specifically in pediatric liver transplant patients 14 . We hypothesized that nonadherence was a strong contributor to high Tac IPV. The aim of this study was to investigate the relationship between Tac IPV and adherence in a cohort of children and young adult kidney transplant recipients. 2. Methods 2.1 Study Design and Population This is a prospective, single center, cross-sectional study. This research was approved by the institutional review board of the University of Miami Miller School of Medicine (IRB #20220914). All participants provided informed consent. All pediatric recipients of isolated kidney transplants who presented to Miami Transplant Institute (MTI) for a post-transplant visit from July 2022 to November 2022 were considered eligible for inclusion in the study. Patients < 10 months post-transplant, not on tacrolimus immunosuppression, or with fewer than three tacrolimus levels in the study period were excluded. The standard induction protocol included thymoglobulin on post-transplant day 0, basiliximab on post-transplant day 0 and post-transplant day 3 or 4, along with a steroid taper. Maintenance immunosuppression included tacrolimus and mycophenolate mofetil, with or without prednisone, based on immunologic risk. Sirolimus was selectively added for some recipients to diminish target tacrolimus levels and to limit nephrotoxicity. The goal tacrolimus (or combined tacrolimus and sirolimus) level was 4–6. Adherence data was collected on the day of enrollment during the clinic visit. Baseline and follow-up laboratory data was collected prospectively through November 2023. Historical data on episodes of rejection and dn DSA formation was also collected. All participants were followed for at least 6 months after enrollment. Demographic and clinical characteristics such as sex, age at transplantation, duration after transplant, underlying renal disease, donor source, pre-transplant panel reactive antibody (PRA), human leukocyte antigen (HLA) matching, and insurance type were obtained from the electronic medical record (EMR). 2.2 Composite Adherence Score In addition to the validated Basel Assessment of Adherence to Immunosuppressive Medical Scale © (BAASIS © ), we also developed a composite adherence score (CAS) which included the BAASIS © to enhance our assessment of adherence. We used a CAS ranging from 0–3 total points based on three parameters: (1) Basel Assessment of Adherence to Immunosuppressive Medical Scale © (BAASIS © ); (2) healthcare team score; and (3) intentionally missed laboratory or clinic visits. Each measure was awarded 1 point if considered nonadherent. The final CAS score was 0 to 3 (See Supplementary Table). A perfect adherence score corresponded to a total score of 0, and nonadherence was defined as a score of 1–3. 2.2.1 BAASIS © The BAASIS © is a written questionnaire that is widely used in research and clinical practice, and has been validated in kidney transplant recipients to assess adherence to immunosuppressive medications 32 – 34 . It consists of five questions on timing and taking of immunosuppressive medications, including missed doses, drug holidays, time deviation, and dose changes or discontinuation of the medications without physician consultation. The questionnaire was filled out independently by the patient (if ≥ 15 years old) or caregiver (if younger) at the time of enrollment, based on a 4-week recall. Nonadherence was defined as “yes” to any of the questions. 2.2.2 Healthcare Team Score The transplant clinical team (three physicians, two nurse coordinators, and one nurse practitioner closely involved in the follow-up care of the kidney transplant recipients) scored recipients’ adherence on 4-point scale (poor, suboptimal, fair, good), as described by Schafer et al 27 . A patient received a score of 4 if all clinicians estimated his/her adherence as good, a score of 2 or 3 if any of the providers estimated his/her adherence as less than good (fair or suboptimal), but not poor, and a score of 1 if any clinician estimated his/her adherence as poor, independently of the estimations given by the other clinicians. A perfect adherence score corresponded to a total score of 4, and nonadherence was defined as a score of 1–3. 2.2.3 Intentionally missed laboratory or clinic visits Transplant nurse coordinators track missed clinic and laboratory visits as a standard. Nonadherence was defined as report of more than one intentionally missed clinic and/or laboratory visit. An intentionally missed visit was defined as patient and/or caregiver not providing an explanation for missing the visit, not trying to reschedule the visit, and/or not calling the healthcare team prior to missing the visit. 2.3 Tacrolimus Intrapatient Variability We collected all available 12-hour trough tacrolimus levels in the six to twelve-month period following enrollment. Levels drawn while hospitalized, during sickness, or non-trough levels were excluded. Tac IPV was calculated using the CV according to the equation CV = σ / µ x 100%, where σ is the standard deviation of the tacrolimus levels and µ is the mean tacrolimus level. 2.4 Graft Outcomes We stratified our cohort by adherence into adherent versus nonadherent groups and by Tac IPV into high versus low Tac IPV. High Tac IPV was defined as ≥ 30%, which has correlated with inferior graft outcomes in prior pediatric and adult studies 8 , 12 , 20 , 35 . To evaluate graft outcomes, we collected data on estimated glomerular filtration rate (GFR), development of proteinuria, history of dn DSA formation, and history of biopsy-proven rejection. GFR was estimated by the creatinine-based “Bedside Schwartz” formula (2009), calculated at the end of the follow-up period. Proteinuria was defined as a urine protein to creatinine ratio above 0.5 mg/mg that persisted over 3 months, on random urine samples collected during the study period. We collected data on dn DSA formation and biopsy-proven rejections from 3 months after kidney transplantation to the end of the study period. DSAs are routinely screened at least yearly in our kidney transplant program, and when clinically indicated. Cutoff for positive reaction was ≥ 3,000 mean fluorescence intensity value, measured by single antigen assay. Biopsies were obtained if there was a clinical concern and analyzed by a transplant nephropathologist according to the consensus guidelines of the most recent international Banff 19 Classification criteria. T-cell mediated rejection (TCMR) was defined as Banff 1A or greater. Antibody-mediated rejection (ABMR) was defined as active ABMR or histologically by C4d staining of peritubular capillaries. Borderline findings were not included. 2.5 Statistical Analysis Baseline characteristics and demographics were summarized descriptively using median (interquartile ranges) and counts (percentages) where appropriate. Mann-Whitney U test was used to compare Tac IPV when stratified by adherence and graft outcomes. Correlation between Tac IPV and CAS was assessed by Spearman’s rank correlation. Chi-squared tests were used to compare patient characteristics and graft outcomes between adherent and nonadherent groups, and between high and low Tac IPV groups. Survival curves were estimated using the Kaplan-Meier method and compared using the log rank test. A p value < 0.05 was considered statistically significant. Analyses were performed using GraphPad Prism 10.0 software. 3. Results 3.1 Study Population From July 2022 through November 2022, 75 patients were enrolled and followed through November 2023, with a total of 725 tacrolimus levels (median, 9 levels per patient; interquartile range 6,13) analyzed. Twelve patients did not meet inclusion criteria. No eligible patient refused to participate. The median follow-up time was 12 months (IQR 10,12). Median age at transplant was 14 years (IQR 7.5,16.5). The median post-transplant time at enrollment was 3.1 years (IQR 1.5,15.2). Demographic characteristics are summarized in Table 1 . In addition to tacrolimus, 28% of participants were also on sirolimus (19 patients) or abatacept (2 patients). All participants continued tacrolimus throughout the study period. 3.2 Tacrolimus Intrapatient Variability and Adherence The median Tac IPV among all participants was 24% (IQR 17,33). Among participants who were not on sirolimus or abatacept, the median tacrolimus level was 5.5 ng/mL (IQR 4.5,6.7). Among patients on concomitant sirolimus or abatacept, and therefore with lower tacrolimus goals, the median tacrolimus level was 3.2 ng/mL (IQR 2.5,4.3). Using the BAASIS © alone, the nonadherence rate was 29%; the nonadherent group had a median Tac IPV of 32%, versus 22% among the adherent cohort (p < 0.001, Fig. 1a). Tac IPV had a positive correlation with BAASIS © score [r = 0.36, (95% CI 0.14 to 0.54, p < 0.01)]. Using the CAS, the nonadherence rate was 49%; the nonadherent group had a significantly higher median Tac IPV of 31%, as compared to the adherent cohort with a median Tac IPV of 20% (p < 0.001, Fig. 1b). Tac IPV also had a positive correlation with CAS [r = 0.44, (95% CI 0.23 to 0.61, p < 0.001)], stronger than with the BAASIS © alone. 3.3 Tacrolimus IPV, Adherence and Graft Outcomes The nonadherent group was more likely to have a history of of biopsy-proven TCMR (p < 0.001) and dn DSA formation (p < 0.001), with a lower eGFR at the end of the study period (p < 0.01), when compared to the adherent cohort. Patients with a high Tac IPV were more likely to have a history of biopsy-proven TCMR (p < 0.001), biopsy-proven ABMR (p = 0.01) and dn DSA formation (p = 0.01), as compared to those with low Tac IPV (Table 2 ). Significant negative correlations were noted between Tac IPV and GFR at the end of the study [r = -0.38, (95% CI -0.52 to -0.09, p < 0.001)], as well as CAS and GFR [r = -0.45, (95% CI -0.62 to -0.25, p < .001)]. Additionally, Tac IPV was higher in both Class I and Class II dn DSA formers, and in patients with a history of biopsy-proven TCMR. Median Tac IPV was 28% (IQR 21,39) in patients with Class I dn DSA formation, as compared to 20% (IQR 15,26) in those without Class I dn DSA formation (p < 0.001). The median Tac IPV was 30% (IQR 23,42) in patients with Class II dn DSA formation, as compared to 19% (IQR 15,24) in those without Class II dn DSA formation (p < 0.001). Median Tac IPV was 37% (IQR 25,44) in patients with a history of biopsy-proven TCMR, as compared to a median Tac IPV of 22% (IQR 16,29) in those with no history of biopsy-proven TCMR (p < 0.001). Further patient characteristics and graft outcomes stratified by adherence and Tac IPV are shown in Table 2 . The Kaplan-Meier curves in Fig. 2 demonstrate the relationship of time to biopsy-proven rejection (Fig. 2a) and time to dn DSA formation (Fig. 2b) after transplant in patients with a high Tac IPV, as compared to those with low Tac IPV (p = 0.001, p = 0.002, respectively). 4. Discussion Our prospective study demonstrated an association between adherence and Tac IPV among pediatric kidney transplant recipients. Our findings complement previous studies that have shown worse renal graft outcomes in patients with a high Tac IPV, as compared to patients with a low Tac IPV 1 , 4 , 5 , 36 , 37 . The results of our study are also in line with the MALT study, showing that tacrolimus variability is a marker of medication nonadherence 14 . The long-term care of children and young adults after kidney transplantation requires a multidisciplinary approach to ensure optimal outcomes. Central to that is the identification and mitigation of barriers to adherence to immunosuppressive therapy. Sufficient evidence now supports that high Tac IPV is indicative of nonadherence and correlates with increased risks of acute rejection and graft loss in kidney transplant recipients 8 , 35 , 38 . Tac IPV can serve as a risk biomarker in children after kidney transplantation, as a proxy for nonadherence, and as a valuable adjunctive tool for clinicians in identifying patients at risk of adverse graft outcomes. There is evidence available that interventions targeted at lowering Tac IPV in patients with high IPV by improving adherence, such as home electronic monitoring, phone applications, and motivational messages, holds promise for optimizing clinical results. McGillicuddy et al. recently conducted a trial investigating a new adherence intervention for Tac IPV in kidney transplant patients 24 . Results showed a significant reduction in Tac IPV among the intervention group (p = 0.046), and a significant improvement in the proportion achieving lower Tac IPV (p = 0.001) compared to controls 24 . This trial highlights the modifiability of Tac IPV through targeted interventions, offering promise for improved clinical outcomes. In children, Hooper et al. conducted a quality improvement study in children after kidney transplantation in a single center 39 . In our prospective study, we showed that Tac IPV had a significant positive correlation with the degree of nonadherence. Furthermore, patients with high Tac IPV were more likely to have a history of biopsy-proven rejection and dn DSA formation. This finding suggests that non-adherent behavior has likely been persistent over time. The median Tac IPV in the entire cohort was 24%, lower than studies that did not censor out data 12 , and higher than other studies assessing highly adherent patients 12 , 26 , 27 , 40 . Median Tac IPV was lower in adherent patients, 20% versus 31% in nonadherent. Leino et al. evaluated baseline patterns of Tac IPV in an adherent cohort of adult kidney and liver transplant recipients. The study population demonstrated 99.9% adherence, as measured by patient daily diary, pill counts and the electronic medication event monitoring system (MEMS); the median weekly Tac IPV was calculated at 15.2% 40 . This finding indirectly suggests that tacrolimus levels are not variable in adherent patients. In a post-hoc analysis of a dataset from a randomized controlled trial, Ko et al. looked at the relationship between adherence, as measured by self-report and MEMS, and Tac IPV in adult kidney transplant recipients. The median Tac IPV was not significantly different between adherent and nonadherent groups, 16 versus 16.5% 26 . This was concordant with a paper by Gokoel et al. that also showed a lower mean Tac IPV of 17.9% and no relationship between adherence and Tac IPV among stable adult kidney transplant recipients 25 . The baseline Tac IPV in our cohort was higher than in these recent adult studies, suggesting an underlying difference 25 , 26 , 40 . The participants in these randomized contThe baseline Tac IPV in a recent pediatric study from Piburn, et al. was 30%, higher than in our study, which is likely explained by their retrospective design as well as inclusion of all uncensored trough levels 12 . A highly variable drug level has been defined in many studies as ≥ 30%, but in the two studies by Ko et al. and Gokoel et al., median Tac IPV was low, probably because the degree of nonadherence was not sufficient in these cohorts to test the hypothesis 25 , 26 . Given the difficulty to engage nonadherent patients in research, trials are often biased towards a sample of adherent patients as supported by a recent systematic analysis 28 . In the study by Ko et al., the cohort consisted of motivated patients that participated in a randomized controlled trial, with a mean age at transplant of 43 years 26 . In our cohort, almost half of the patients were nonadherent at a median age of 17 years. It is well known that recipients aged 14 to 16 years have the greatest risk of kidney graft failure 1 , 5 , 16 , 41 . As described by Piburn et al., the baseline trend of Tac IPV started to increase in adolescence and young adulthood, which could indicate an increased incidence of nonadherence by this age group 12 . In our cohort, nonadherent patients had a higher Tac IPV and were more likely to have a history of biopsy-proven rejection and formation of dn DSAs, suggesting that nonadherent behavior probably preceded our assessment. We identified an association between adherence and Tac IPV, where the nonadherent group demonstrated a high-risk Tac IPV of ≥ 30%, which had not been previously done prospectively. One of the largest strengths of this study was the prospective study design, and the real time collection of data, allowing us to limit confounders such as improper timed levels, levels drawn during hospitalization or comorbid illness, or a change in therapeutic goal during acute infection or graft rejection. Limitations include the lack of objective measures of adherence. This highlights the difficulty truly assessing adherence in the clinical setting and in reasearch. The composite score has not yet been validated, which limits our study; however, the BAASIS © is a validated measure used among kidney transplant recipients to assess adherence to immunosuppressive medications and is a component of our CAS 32 – 34 , 42 . The study was not blinded; therefore, the providers scoring for nonadherence were also caring for the patients, allowing them to make informed assessments of adherence, but also allowing for a possibility of bias. Our study was further limited by being from a single center and having a relatively short follow-up time. To conclude, Tac IPV was significantly higher in nonadherent patients, and high Tac IPV was associated with inferior graft outcomes. Adolescents often demonstrate lapses in medication adherence and overestimate their ability to take on this responsibility, making recognition of nonadherent behavior challenging. Therefore, an objective measure like Tac IPV could be a helpful tool in the clinic setting as a more accurate assessment of adherence. Tac IPV could be a modifiable risk factor allowing for early detection of inconsistent immunosuppression, flag high risk patients and thereby allow for timely interventions to prevent adverse graft outcomes. Future prospective studies of pediatric kidney transplant recipients are needed to validate the efficacy of using Tac IPV as a biomarker of adverse graft outcomes and test modifiability to improve graft outcomes. Abbreviations 95% CI (95% confidence interval), ABMR (antibody-mediated rejection), BAASIS Ó (Basel Assessment of Adherence to Immunosuppressive Medical Scale), CAS (composite adherence score), CV (coefficient of variation), dn DSA ( de novo donor-specific antibody), eGFR (estimated glomerular filtration rate), EMR (electronic medical record), ESKD (end-stage kidney disease), HLA (human leukocyte antigen), IQR (interquartile range), MTI (Miami Transplant Institute), PRA (panel reactive antibody), RCT (randomized controlled trial), Tac IPV (tacrolimus intrapatient variability), TCMR (T-cell mediated rejection) Declarations The authors of this manuscript have no conflicts of interest to disclose as described by Pediatric Nephrology . 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American Journal of Transplantation . 2008;8(3):616-626. doi:10.1111/j.1600-6143.2007.02127.x Duncan S, Annunziato RA, Dunphy C, LaPointe Rudow D, Shneider BL, Shemesh E. A systematic review of immunosuppressant adherence interventions in transplant recipients: Decoding the streetlight effect. Pediatr Transplant . 2018;22(1). doi:10.1111/petr.13086 Osterberg L, Blaschke T. Adherence to Medication. New England Journal of Medicine . 2005;353(5):487-497. doi:10.1056/NEJMra050100 Butler JA, Peveler RC, Roderick P, Horne R, Mason JC. Measuring compliance with drug regimens after renal transplantation: comparison of self-report and clinician rating with electronic monitoring. Transplantation . 2004;77(5):786-789. doi:10.1097/01.TP.0000110412.20050.36 De Geest S, Vanhaecke J. Methodological issues in transplant compliance research. Transplant Proc . 1999;31(4):81S-83S. doi:10.1016/S0041-1345(99)00137-2 Denhaerynck K, Dobbels F, Košťálová B, De Geest S. Psychometric Properties of the BAASIS: A Meta-analysis of Individual Participant Data. Transplantation . 2023;107(8):1795-1809. doi:10.1097/TP.0000000000004574 Dobbels F, Berben L, De Geest S, et al. The Psychometric Properties and Practicability of Self-Report Instruments to Identify Medication Nonadherence in Adult Transplant Patients: A Systematic Review. Transplantation . 2010;90(2):205-219. doi:10.1097/TP.0b013e3181e346cd Marsicano E de O, Fernandes N da S, Colugnati F, et al. Transcultural adaptation and initial validation of Brazilian-Portuguese version of the Basel assessment of adherence to immunosuppressive medications scale (BAASIS) in kidney transplants. BMC Nephrol . 2013;14(1):108. doi:10.1186/1471-2369-14-108 Rodrigo E, Segundo DS, Fernández-Fresnedo G, et al. Within-Patient Variability in Tacrolimus Blood Levels Predicts Kidney Graft Loss and Donor-Specific Antibody Development. Transplantation . 2016;100(11):2479-2485. doi:10.1097/TP.0000000000001040 O’Regan JA, Canney M, Connaughton DM, et al. Tacrolimus trough-level variability predicts long-term allograft survival following kidney transplantation. J Nephrol . 2016;29(2):269-276. doi:10.1007/s40620-015-0230-0 Prytula AA, Bouts AH, Mathot RAA, et al. Intra‐patient variability in tacrolimus trough concentrations and renal function decline in pediatric renal transplant recipients. Pediatr Transplant . 2012;16(6):613-618. doi:10.1111/j.1399-3046.2012.01727.x Borra LCP, Roodnat JI, Kal JA, Mathot RAA, Weimar W, van Gelder T. High within-patient variability in the clearance of tacrolimus is a risk factor for poor long-term outcome after kidney transplantation. Nephrology Dialysis Transplantation . 2010;25(8):2757-2763. doi:10.1093/ndt/gfq096 Hooper DK, Varnell CD, Rich K, et al. A Medication Adherence Promotion System to Reduce Late Kidney Allograft Rejection: A Quality Improvement Study. American Journal of Kidney Diseases . 2022;79(3):335-346. doi:10.1053/j.ajkd.2021.06.021 Leino AD, King EC, Jiang W, et al. Assessment of tacrolimus intrapatient variability in stable adherent transplant recipients: Establishing baseline values. American Journal of Transplantation . 2019;19(5):1410-1420. doi:10.1111/ajt.15199 Van Arendonk KJ, James NT, Boyarsky BJ, et al. Age at Graft Loss after Pediatric Kidney Transplantation. Clinical Journal of the American Society of Nephrology . 2013;8(6):1019-1026. doi:10.2215/CJN.10311012 Lieb M, Hepp T, Schiffer M, Opgenoorth M, Erim Y. Accuracy and concordance of measurement methods to assess non-adherence after renal transplantation - a prospective study. BMC Nephrol . 2020;21(1):114. doi:10.1186/s12882-020-01781-1 Tables TABLE 1. Characteristics of pediatric transplant recipients at adherence assessment Characteristic N=75 Age at enrollment (years), median [IQR] 17 [12,19] Sex, n (%) Female Male 24 (32) 51 (68) Race, Ethnicity, n (%) Black, Non-Hispanic Black, Hispanic White, Non-Hispanic White, Hispanic Asian, Non-Hispanic 27 (36) 1 (1) 7 (9) 38 (51) 2 (3) Insurance type, n (%) Medicaid Medicare Private Insurance 39 (52) 18 (24) 18 (24) Cause of ESKD, n (%) Congenital/Inherited Glomerular Other 36 (48) 32 (43) 7 (9) Donor status, n (%) Living Donor Deceased Donor 28 (37) 47 (63) HLA mismatch, n (%) HLA match 0-1 HLA match 2-3 HLA match 4-6 38 (51) 28 (37) 9 (12) Number of tacrolimus levels per patient, median [IQR] 9 [6,13] ESKD: End-stage kidney disease; HLA: human leukocyte antigen TABLE 2 . Comparison of adherent and nonadherent patients, and patients with IPV <30% and ≥30% (n=75) Characteristic Adherent Nonadherent p-value IPV < 30% IPV ≥ 30% p-value N (%) 38 (51) 37 (49) 50 (67) 25 (33) Age at enrollment (years) , median [IQR] 17 [12,19] 16 [13,20] 0.84 17 [12,19] 16 [13,20] 0.89 Sex , n (%) Female, n (%) Male, n (%) 10 (26) 28 (74) 14 (38) 23 (62) 0.33 14 (28) 36 (72) 10 (40) 15 (60) 0.29 Race, Ethnicity , n (%) Black, Non-Hispanic, n (%) Black, Hispanic, n (%) White, Non-Hispanic, n (%) White, Hispanic, n (%) Asian, Non-Hispanic, n (%) 10 (26) 0 (0) 4 (11) 22 (58) 2 (5) 17 (46) 1 (3) 3 (8) 16 (43) 0 (0) 0.21 16 (32) 1 (2) 4 (8) 28 (56) 1 (2) 11 (44) 0 (0) 3 (12) 10 (40) 1 (4) 0.69 Time since transplant (years) , median [IQR] 3 [1,5] 4 [2,6] 0.31 3 [2,5] 3 [2,5] 0.86 Insurance type , n (%) Medicaid, n (%) Medicare, n (%) Private Insurance, n (%) 16 (42) 12 (32) 10 (26) 23 (62) 6 (16) 8 (22) 0.18 23 (46) 12 (24) 15 (30) 16 (64) 6 (24) 3 (12) 0.19 History of TCMR , n (%) 4 (10) 17 (46) < 0.001 7 (14) 13 (52) < 0.001 History of ABMR , n (%) 1 (3) 5 (10) 0.11 1 (2) 5 (20) 0.01 Proteinuria , n (%) 3 (8) 6 (16) 0.31 4 (8) 5 (20) 0.15 History of dn DSA Class II , n (%) 16 (42) 25 (68) < 0.001 23 (46) 19 (76) 0.01 IPV (%) , median [IQR] 20 [15,26] 31 [23,43] < 0.001 20 [15,24] 39 [33,44] < 0.001 CAS , median [IQR] 0 [0] 2 [1,4] < 0.001 0 [0,1] 2 [1,4] 0.001 IPV: intrapatient variability; TCMR: T-cell mediated rejection; ABMR: antibody-mediated rejection; proteinuria: urine protein/creatinine ratio above 0.5 mg/mg that persisted over 3 months; dn DSA: de novo donor-specific antibodies; CAS: composite adherence score Supplementary Files SUPPLEMENTARYTABLE.docx Supporting information statement Additional supporting information may be found online in the Supporting Information section. 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Gavcovich","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Tara","middleName":"B.","lastName":"Gavcovich","suffix":""},{"id":371406479,"identity":"331f5c12-2973-4854-9bce-7956ff07e426","order_by":2,"name":"Marissa J. DeFreitas","email":"","orcid":"","institution":"University of Miami Miller School of Medicine: University of Miami School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Marissa","middleName":"J.","lastName":"DeFreitas","suffix":""},{"id":371406480,"identity":"a7653e5a-273c-4a94-be88-6e3852ee0536","order_by":3,"name":"Claudia Serrano","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Serrano","suffix":""},{"id":371406481,"identity":"8f618443-5c65-4bde-a302-3af59bab22cb","order_by":4,"name":"Esther Rivas","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Rivas","suffix":""},{"id":371406482,"identity":"9e162475-f382-4430-a32c-ae65c750019a","order_by":5,"name":"Migdalia Jorge","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Migdalia","middleName":"","lastName":"Jorge","suffix":""},{"id":371406483,"identity":"a41a1629-6f59-4439-a559-f0dad839473d","order_by":6,"name":"Wacharee Seeherunvong","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Wacharee","middleName":"","lastName":"Seeherunvong","suffix":""},{"id":371406484,"identity":"b704b616-5fb4-4651-b89a-1a5db1dc97c7","order_by":7,"name":"Chryso Katsoufis","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Chryso","middleName":"","lastName":"Katsoufis","suffix":""},{"id":371406485,"identity":"8e4b5d0c-c080-41c8-a03c-a444be3fe7d1","order_by":8,"name":"Wendy Glaberson","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"","lastName":"Glaberson","suffix":""},{"id":371406486,"identity":"5eb7e003-79f9-4330-a1be-869f4274d57a","order_by":9,"name":"Melisa Oliva","email":"","orcid":"","institution":"Jackson Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Melisa","middleName":"","lastName":"Oliva","suffix":""},{"id":371406487,"identity":"b7b9969a-510b-41f3-b627-db3e063acb2b","order_by":10,"name":"Adela D. Mattiazzi","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Adela","middleName":"D.","lastName":"Mattiazzi","suffix":""},{"id":371406488,"identity":"7c49eee7-7178-4269-9f21-bf31b5cafcb3","order_by":11,"name":"Carolyn Abitbol","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Carolyn","middleName":"","lastName":"Abitbol","suffix":""},{"id":371406489,"identity":"83f53a21-c429-42f6-82ab-cab5e9557a4b","order_by":12,"name":"Jayanthi Chandar","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Jayanthi","middleName":"","lastName":"Chandar","suffix":""}],"badges":[],"createdAt":"2024-10-25 22:17:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5334772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5334772/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/frtra.2025.1572928","type":"published","date":"2025-03-16T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68204390,"identity":"1fcedc1b-5a87-427b-a7a8-bd2039cce0ba","added_by":"auto","created_at":"2024-11-04 16:07:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150055,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5334772/v1/bb41360098bccc6860491372.png"},{"id":68205772,"identity":"8186f08d-3e1c-4c98-bf91-1d8ea36562dd","added_by":"auto","created_at":"2024-11-04 16:15:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161621,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5334772/v1/90b616199d2ffee615157f3e.png"},{"id":78851033,"identity":"f48f54c7-34df-4538-b9c3-5e8df1e44c49","added_by":"auto","created_at":"2025-03-19 19:23:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5334772/v1/4a4007b3-8ec2-41fe-8567-ba17ea0dd470.pdf"},{"id":68204388,"identity":"27634fd0-a73d-4828-85c5-8bd68c60260f","added_by":"auto","created_at":"2024-11-04 16:07:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupporting information statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditional supporting information may be found online in the Supporting Information section.\u003c/p\u003e","description":"","filename":"SUPPLEMENTARYTABLE.docx","url":"https://assets-eu.researchsquare.com/files/rs-5334772/v1/0c7ac7f875df8d114cd1c49a.docx"}],"financialInterests":"","formattedTitle":"Intrapatient Tacrolimus Variability is Associated with Medical Nonadherence among Pediatric Kidney Transplant Recipients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAllograft rejection due to insufficient immunosuppression is a major contributor to early graft loss\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Not taking medications consistently or inconsistent follow-up with the medical care team is a known risk factor for poor graft outcomes, with nonadherence being the most common in adolescent patients\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Post-transplant maintenance immunosuppression typically includes tacrolimus, a calcineurin inhibitor, with a narrow therapeutic index requiring frequent drug level monitoring to balance effective drug concentrations while minimizing toxicity\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. High tacrolimus intrapatient variability (Tac IPV) has been studied for over two decades and measured with two different methods, the coefficient of variation (CV) and Medication Level Variability Index (MLVI). Tac IPV has been increasingly recognized as a biomarker for graft rejection and loss\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and a marker of nonadherence\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The Medication Adherence in children who had a Liver Transplant (MALT) prospective multi-site study evaluated whether MLVI predicts late acute rejection. A total of 379 participants were followed prospectively and results showed that a higher prerejection MLVI predicted adverse graft outcomes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe International Consensus on Managing Modifiable Risk in Transplantation recommends monitoring nonadherence as a fifth vital sign\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. As expected, nonadherence has also been associated with worse graft outcomes, leading to increased incidence of rejection, \u003cem\u003ede novo\u003c/em\u003e donor specific antibody (\u003cem\u003edn\u003c/em\u003eDSA) formation, decreased renal function and ultimately graft loss\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While multiple factors contribute to raising the Tac IPV, including tube feeding, feeding intolerance, infection, drug or food interactions and dose adjustments\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, medication nonadherence is thought to be the strongest contributor\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Several studies have shown that tacrolimus variability responds to behavioral interventions strongly supporting that is affected by behavior\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This relationship, however, has not been described well in adults or children, especially since nonadherence is difficult to measure consistently in the clinical setting\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The prevalence of nonadherence varies across studies, and depends on heterogenous measurement tools, which adds to the inconsistent analyses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Measures of nonadherence include direct (observation, drug assays) and indirect (self-report, collateral report, prescription refills, electronic monitoring) parameters, and there is no single ideal method given the limitations of each\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince adherence measures can be quite unreliable and burdensome to patients and clinicians, tacrolimus variability has been suggested as a potential objective biomarker for nonadherence, specifically in pediatric liver transplant patients\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. We hypothesized that nonadherence was a strong contributor to high Tac IPV. The aim of this study was to investigate the relationship between Tac IPV and adherence in a cohort of children and young adult kidney transplant recipients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis is a prospective, single center, cross-sectional study. This research was approved by the institutional review board of the University of Miami Miller School of Medicine (IRB #20220914). All participants provided informed consent. All pediatric recipients of isolated kidney transplants who presented to Miami Transplant Institute (MTI) for a post-transplant visit from July 2022 to November 2022 were considered eligible for inclusion in the study. Patients\u0026thinsp;\u0026lt;\u0026thinsp;10 months post-transplant, not on tacrolimus immunosuppression, or with fewer than three tacrolimus levels in the study period were excluded. The standard induction protocol included thymoglobulin on post-transplant day 0, basiliximab on post-transplant day 0 and post-transplant day 3 or 4, along with a steroid taper. Maintenance immunosuppression included tacrolimus and mycophenolate mofetil, with or without prednisone, based on immunologic risk. Sirolimus was selectively added for some recipients to diminish target tacrolimus levels and to limit nephrotoxicity. The goal tacrolimus (or combined tacrolimus and sirolimus) level was 4\u0026ndash;6. Adherence data was collected on the day of enrollment during the clinic visit. Baseline and follow-up laboratory data was collected prospectively through November 2023. Historical data on episodes of rejection and \u003cem\u003edn\u003c/em\u003eDSA formation was also collected. All participants were followed for at least 6 months after enrollment. Demographic and clinical characteristics such as sex, age at transplantation, duration after transplant, underlying renal disease, donor source, pre-transplant panel reactive antibody (PRA), human leukocyte antigen (HLA) matching, and insurance type were obtained from the electronic medical record (EMR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Composite Adherence Score\u003c/h2\u003e \u003cp\u003eIn addition to the validated Basel Assessment of Adherence to Immunosuppressive Medical Scale\u003csup\u003e\u0026copy;\u003c/sup\u003e(BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e), we also developed a composite adherence score (CAS) which included the BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e to enhance our assessment of adherence. We used a CAS ranging from 0\u0026ndash;3 total points based on three parameters: (1) Basel Assessment of Adherence to Immunosuppressive Medical Scale\u003csup\u003e\u0026copy;\u003c/sup\u003e(BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e); (2) healthcare team score; and (3) intentionally missed laboratory or clinic visits. Each measure was awarded 1 point if considered nonadherent. The final CAS score was 0 to 3 (See Supplementary Table). A perfect adherence score corresponded to a total score of 0, and nonadherence was defined as a score of 1\u0026ndash;3.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e2.2.1 BAASIS\u003c/span\u003e\u003csup\u003e\u0026copy;\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eThe BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e is a written questionnaire that is widely used in research and clinical practice, and has been validated in kidney transplant recipients to assess adherence to immunosuppressive medications\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. It consists of five questions on timing and taking of immunosuppressive medications, including missed doses, drug holidays, time deviation, and dose changes or discontinuation of the medications without physician consultation. The questionnaire was filled out independently by the patient (if\u0026thinsp;\u0026ge;\u0026thinsp;15 years old) or caregiver (if younger) at the time of enrollment, based on a 4-week recall. Nonadherence was defined as \u0026ldquo;yes\u0026rdquo; to any of the questions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Healthcare Team Score\u003c/h2\u003e \u003cp\u003eThe transplant clinical team (three physicians, two nurse coordinators, and one nurse practitioner closely involved in the follow-up care of the kidney transplant recipients) scored recipients\u0026rsquo; adherence on 4-point scale (poor, suboptimal, fair, good), as described by Schafer et al\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. A patient received a score of 4 if all clinicians estimated his/her adherence as good, a score of 2 or 3 if any of the providers estimated his/her adherence as less than good (fair or suboptimal), but not poor, and a score of 1 if any clinician estimated his/her adherence as poor, independently of the estimations given by the other clinicians. A perfect adherence score corresponded to a total score of 4, and nonadherence was defined as a score of 1\u0026ndash;3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Intentionally missed laboratory or clinic visits\u003c/h2\u003e \u003cp\u003eTransplant nurse coordinators track missed clinic and laboratory visits as a standard. Nonadherence was defined as report of more than one intentionally missed clinic and/or laboratory visit. An intentionally missed visit was defined as patient and/or caregiver not providing an explanation for missing the visit, not trying to reschedule the visit, and/or not calling the healthcare team prior to missing the visit.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.3 Tacrolimus Intrapatient Variability\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWe collected all available 12-hour trough tacrolimus levels in the six to twelve-month period following enrollment. Levels drawn while hospitalized, during sickness, or non-trough levels were excluded. Tac IPV was calculated using the CV according to the equation CV\u0026thinsp;=\u0026thinsp;σ / \u0026micro; x 100%, where σ is the standard deviation of the tacrolimus levels and \u0026micro; is the mean tacrolimus level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.4 Graft Outcomes\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWe stratified our cohort by adherence into adherent versus nonadherent groups and by Tac IPV into high versus low Tac IPV. High Tac IPV was defined as \u0026ge;\u0026thinsp;30%, which has correlated with inferior graft outcomes in prior pediatric and adult studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To evaluate graft outcomes, we collected data on estimated glomerular filtration rate (GFR), development of proteinuria, history of \u003cem\u003edn\u003c/em\u003eDSA formation, and history of biopsy-proven rejection. GFR was estimated by the creatinine-based \u0026ldquo;Bedside Schwartz\u0026rdquo; formula (2009), calculated at the end of the follow-up period. Proteinuria was defined as a urine protein to creatinine ratio above 0.5 mg/mg that persisted over 3 months, on random urine samples collected during the study period. We collected data on \u003cem\u003edn\u003c/em\u003eDSA formation and biopsy-proven rejections from 3 months after kidney transplantation to the end of the study period. DSAs are routinely screened at least yearly in our kidney transplant program, and when clinically indicated. Cutoff for positive reaction was \u0026ge;\u0026thinsp;3,000 mean fluorescence intensity value, measured by single antigen assay. Biopsies were obtained if there was a clinical concern and analyzed by a transplant nephropathologist according to the consensus guidelines of the most recent international Banff 19 Classification criteria. T-cell mediated rejection (TCMR) was defined as Banff 1A or greater. Antibody-mediated rejection (ABMR) was defined as active ABMR or histologically by C4d staining of peritubular capillaries. Borderline findings were not included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.5 Statistical Analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eBaseline characteristics and demographics were summarized descriptively using median (interquartile ranges) and counts (percentages) where appropriate. Mann-Whitney U test was used to compare Tac IPV when stratified by adherence and graft outcomes. Correlation between Tac IPV and CAS was assessed by Spearman\u0026rsquo;s rank correlation. Chi-squared tests were used to compare patient characteristics and graft outcomes between adherent and nonadherent groups, and between high and low Tac IPV groups. Survival curves were estimated using the Kaplan-Meier method and compared using the log rank test. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Analyses were performed using GraphPad Prism 10.0 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Population\u003c/h2\u003e \u003cp\u003e From July 2022 through November 2022, 75 patients were enrolled and followed through November 2023, with a total of 725 tacrolimus levels (median, 9 levels per patient; interquartile range 6,13) analyzed. Twelve patients did not meet inclusion criteria. No eligible patient refused to participate. The median follow-up time was 12 months (IQR 10,12). Median age at transplant was 14 years (IQR 7.5,16.5). The median post-transplant time at enrollment was 3.1 years (IQR 1.5,15.2). Demographic characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In addition to tacrolimus, 28% of participants were also on sirolimus (19 patients) or abatacept (2 patients). All participants continued tacrolimus throughout the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Tacrolimus Intrapatient Variability and Adherence\u003c/h2\u003e \u003cp\u003eThe median Tac IPV among all participants was 24% (IQR 17,33). Among participants who were not on sirolimus or abatacept, the median tacrolimus level was 5.5 ng/mL (IQR 4.5,6.7). Among patients on concomitant sirolimus or abatacept, and therefore with lower tacrolimus goals, the median tacrolimus level was 3.2 ng/mL (IQR 2.5,4.3).\u003c/p\u003e \u003cp\u003eUsing the BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e alone, the nonadherence rate was 29%; the nonadherent group had a median Tac IPV of 32%, versus 22% among the adherent cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1a). Tac IPV had a positive correlation with BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e score [r\u0026thinsp;=\u0026thinsp;0.36, (95% CI 0.14 to 0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)].\u003c/p\u003e \u003cp\u003eUsing the CAS, the nonadherence rate was 49%; the nonadherent group had a significantly higher median Tac IPV of 31%, as compared to the adherent cohort with a median Tac IPV of 20% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1b). Tac IPV also had a positive correlation with CAS [r\u0026thinsp;=\u0026thinsp;0.44, (95% CI 0.23 to 0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)], stronger than with the BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Tacrolimus IPV, Adherence and Graft Outcomes\u003c/h2\u003e \u003cp\u003eThe nonadherent group was more likely to have a history of of biopsy-proven TCMR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003edn\u003c/em\u003eDSA formation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a lower eGFR at the end of the study period (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), when compared to the adherent cohort. Patients with a high Tac IPV were more likely to have a history of biopsy-proven TCMR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), biopsy-proven ABMR (p\u0026thinsp;=\u0026thinsp;0.01) and \u003cem\u003edn\u003c/em\u003eDSA formation (p\u0026thinsp;=\u0026thinsp;0.01), as compared to those with low Tac IPV (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant negative correlations were noted between Tac IPV and GFR at the end of the study [r = -0.38, (95% CI -0.52 to -0.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)], as well as CAS and GFR [r = -0.45, (95% CI -0.62 to -0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;.001)]. Additionally, Tac IPV was higher in both Class I and Class II \u003cem\u003edn\u003c/em\u003eDSA formers, and in patients with a history of biopsy-proven TCMR. Median Tac IPV was 28% (IQR 21,39) in patients with Class I \u003cem\u003edn\u003c/em\u003eDSA formation, as compared to 20% (IQR 15,26) in those without Class I \u003cem\u003edn\u003c/em\u003eDSA formation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median Tac IPV was 30% (IQR 23,42) in patients with Class II \u003cem\u003edn\u003c/em\u003eDSA formation, as compared to 19% (IQR 15,24) in those without Class II \u003cem\u003edn\u003c/em\u003eDSA formation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Median Tac IPV was 37% (IQR 25,44) in patients with a history of biopsy-proven TCMR, as compared to a median Tac IPV of 22% (IQR 16,29) in those with no history of biopsy-proven TCMR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further patient characteristics and graft outcomes stratified by adherence and Tac IPV are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier curves in Fig.\u0026nbsp;2 demonstrate the relationship of time to biopsy-proven rejection (Fig.\u0026nbsp;2a) and time to \u003cem\u003edn\u003c/em\u003eDSA formation (Fig.\u0026nbsp;2b) after transplant in patients with a high Tac IPV, as compared to those with low Tac IPV (p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.002, respectively).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur prospective study demonstrated an association between adherence and Tac IPV among pediatric kidney transplant recipients. Our findings complement previous studies that have shown worse renal graft outcomes in patients with a high Tac IPV, as compared to patients with a low Tac IPV\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The results of our study are also in line with the MALT study, showing that tacrolimus variability is a marker of medication nonadherence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe long-term care of children and young adults after kidney transplantation requires a multidisciplinary approach to ensure optimal outcomes. Central to that is the identification and mitigation of barriers to adherence to immunosuppressive therapy. Sufficient evidence now supports that high Tac IPV is indicative of nonadherence and correlates with increased risks of acute rejection and graft loss in kidney transplant recipients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Tac IPV can serve as a risk biomarker in children after kidney transplantation, as a proxy for nonadherence, and as a valuable adjunctive tool for clinicians in identifying patients at risk of adverse graft outcomes.\u003c/p\u003e\n\u003cp\u003eThere is evidence available that interventions targeted at lowering Tac IPV in patients with high IPV by improving adherence, such as home electronic monitoring, phone applications, and motivational messages, holds promise for optimizing clinical results. McGillicuddy et al. recently conducted a trial investigating a new adherence intervention for Tac IPV in kidney transplant patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Results showed a significant reduction in Tac IPV among the intervention group (p\u0026thinsp;=\u0026thinsp;0.046), and a significant improvement in the proportion achieving lower Tac IPV (p\u0026thinsp;=\u0026thinsp;0.001) compared to controls\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This trial highlights the modifiability of Tac IPV through targeted interventions, offering promise for improved clinical outcomes. In children, Hooper et al. conducted a quality improvement study in children after kidney transplantation in a single center\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn our prospective study, we showed that Tac IPV had a significant positive correlation with the degree of nonadherence. Furthermore, patients with high Tac IPV were more likely to have a history of biopsy-proven rejection and \u003cem\u003edn\u003c/em\u003eDSA formation. This finding suggests that non-adherent behavior has likely been persistent over time. The median Tac IPV in the entire cohort was 24%, lower than studies that did not censor out data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and higher than other studies assessing highly adherent patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Median Tac IPV was lower in adherent patients, 20% versus 31% in nonadherent.\u003c/p\u003e\n\u003cp\u003eLeino et al. evaluated baseline patterns of Tac IPV in an adherent cohort of adult kidney and liver transplant recipients. The study population demonstrated 99.9% adherence, as measured by patient daily diary, pill counts and the electronic medication event monitoring system (MEMS); the median weekly Tac IPV was calculated at 15.2%\u003csup\u003e40\u003c/sup\u003e. This finding indirectly suggests that tacrolimus levels are not variable in adherent patients. In a post-hoc analysis of a dataset from a randomized controlled trial, Ko et al. looked at the relationship between adherence, as measured by self-report and MEMS, and Tac IPV in adult kidney transplant recipients. The median Tac IPV was not significantly different between adherent and nonadherent groups, 16 versus 16.5%\u003csup\u003e26\u003c/sup\u003e. This was concordant with a paper by Gokoel et al. that also showed a lower mean Tac IPV of 17.9% and no relationship between adherence and Tac IPV among stable adult kidney transplant recipients \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The baseline Tac IPV in our cohort was higher than in these recent adult studies, suggesting an underlying difference\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The participants in these randomized contThe baseline Tac IPV in a recent pediatric study from Piburn, et al. was 30%, higher than in our study, which is likely explained by their retrospective design as well as inclusion of all uncensored trough levels\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA highly variable drug level has been defined in many studies as \u0026ge;\u0026thinsp;30%, but in the two studies by Ko et al. and Gokoel et al., median Tac IPV was low, probably because the degree of nonadherence was not sufficient in these cohorts to test the hypothesis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Given the difficulty to engage nonadherent patients in research, trials are often biased towards a sample of adherent patients as supported by a recent systematic analysis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In the study by Ko et al., the cohort consisted of motivated patients that participated in a randomized controlled trial, with a mean age at transplant of 43 years\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In our cohort, almost half of the patients were nonadherent at a median age of 17 years. It is well known that recipients aged 14 to 16 years have the greatest risk of kidney graft failure\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. As described by Piburn et al., the baseline trend of Tac IPV started to increase in adolescence and young adulthood, which could indicate an increased incidence of nonadherence by this age group\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In our cohort, nonadherent patients had a higher Tac IPV and were more likely to have a history of biopsy-proven rejection and formation of \u003cem\u003edn\u003c/em\u003eDSAs, suggesting that nonadherent behavior probably preceded our assessment. We identified an association between adherence and Tac IPV, where the nonadherent group demonstrated a high-risk Tac IPV of \u0026ge;\u0026thinsp;30%, which had not been previously done prospectively.\u003c/p\u003e\n\u003cp\u003eOne of the largest strengths of this study was the prospective study design, and the real time collection of data, allowing us to limit confounders such as improper timed levels, levels drawn during hospitalization or comorbid illness, or a change in therapeutic goal during acute infection or graft rejection. Limitations include the lack of objective measures of adherence. This highlights the difficulty truly assessing adherence in the clinical setting and in reasearch. The composite score has not yet been validated, which limits our study; however, the BAASIS\u003csup\u003e\u0026copy;\u003c/sup\u003e is a validated measure used among kidney transplant recipients to assess adherence to immunosuppressive medications and is a component of our CAS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The study was not blinded; therefore, the providers scoring for nonadherence were also caring for the patients, allowing them to make informed assessments of adherence, but also allowing for a possibility of bias. Our study was further limited by being from a single center and having a relatively short follow-up time.\u003c/p\u003e\n\u003cp\u003eTo conclude, Tac IPV was significantly higher in nonadherent patients, and high Tac IPV was associated with inferior graft outcomes. Adolescents often demonstrate lapses in medication adherence and overestimate their ability to take on this responsibility, making recognition of nonadherent behavior challenging. Therefore, an objective measure like Tac IPV could be a helpful tool in the clinic setting as a more accurate assessment of adherence. Tac IPV could be a modifiable risk factor allowing for early detection of inconsistent immunosuppression, flag high risk patients and thereby allow for timely interventions to prevent adverse graft outcomes. Future prospective studies of pediatric kidney transplant recipients are needed to validate the efficacy of using Tac IPV as a biomarker of adverse graft outcomes and test modifiability to improve graft outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e95% CI (95% confidence interval), ABMR (antibody-mediated rejection),\u0026nbsp;BAASIS\u003csup\u003e\u0026Oacute;\u003c/sup\u003e (Basel Assessment of Adherence to Immunosuppressive Medical Scale),\u0026nbsp;CAS (composite adherence score), CV (coefficient of variation), \u003cem\u003edn\u003c/em\u003eDSA (\u003cem\u003ede novo\u0026nbsp;\u003c/em\u003edonor-specific antibody), eGFR (estimated glomerular filtration rate), EMR (electronic medical record), ESKD (end-stage kidney disease), HLA (human leukocyte antigen), IQR (interquartile range), MTI (Miami Transplant Institute), PRA (panel reactive antibody), RCT (randomized controlled trial), Tac IPV (tacrolimus intrapatient variability), TCMR (T-cell mediated rejection)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors of this manuscript have no conflicts of interest to disclose as described by \u003cem\u003ePediatric Nephrology\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the sensitive nature of the questions asked in this study, survey respondents were assured raw data would remain confidential and would not be shared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDobbels F, Ruppar T, De Geest S, Decorte A, Van Damme-Lombaerts R, Fine RN. Adherence to the immunosuppressive regimen in pediatric kidney transplant recipients: A systematic review. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2009;14(5):603-613. doi:10.1111/j.1399-3046.2010.01299.x\u003c/li\u003e\n\u003cli\u003eHsu DT. Biological and psychological differences in the child and adolescent transplant recipient. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2005;9(3):416-421. doi:10.1111/j.1399-3046.2005.00352.x\u003c/li\u003e\n\u003cli\u003eDharnidharka VR, Fiorina P, Harmon WE. Kidney Transplantation in Children. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e. 2014;371(6):549-558. doi:10.1056/NEJMra1314376\u003c/li\u003e\n\u003cli\u003eZelikovsky N, Schast AP, Palmer J, Meyers KEC. Perceived barriers to adherence among adolescent renal transplant candidates. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2008;12(3):300-308. doi:10.1111/j.1399-3046.2007.00886.x\u003c/li\u003e\n\u003cli\u003eDenhaerynck K, Steiger J, Bock A, et al. Prevalence and Risk Factors of Non-Adherence with Immunosuppressive Medication in Kidney Transplant Patients. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2007;7(1):108-116. doi:10.1111/J.1600-6143.2006.01611.X\u003c/li\u003e\n\u003cli\u003eStaatz CE, Tett SE. Clinical Pharmacokinetics and Pharmacodynamics of Tacrolimus in Solid Organ Transplantation. \u003cem\u003eClin Pharmacokinet\u003c/em\u003e. 2004;43(10):623-653. doi:10.2165/00003088-200443100-00001\u003c/li\u003e\n\u003cli\u003eShuker N, Van Gelder T, Hesselink DA. Intra-patient variability in tacrolimus exposure: Causes, consequences for clinical management. \u003cem\u003eTransplant Rev\u003c/em\u003e. 2015;29(2):78-84. doi:10.1016/J.TRRE.2015.01.002\u003c/li\u003e\n\u003cli\u003eGonzales HM, McGillicuddy JW, Rohan V, et al. A comprehensive review of the impact of tacrolimus intrapatient variability on clinical outcomes in kidney transplantation. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2020;20(8):1969-1983. doi:10.1111/ajt.16002\u003c/li\u003e\n\u003cli\u003eLarkins N, Matsell DG. Tacrolimus therapeutic drug monitoring and pediatric renal transplant graft outcomes. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2014;18(8):803-809. doi:10.1111/petr.12369\u003c/li\u003e\n\u003cli\u003eWhalen HR, Glen JA, Harkins V, et al. High Intrapatient Tacrolimus Variability Is Associated With Worse Outcomes in Renal Transplantation Using a Low-Dose Tacrolimus Immunosuppressive Regime. \u003cem\u003eTransplantation\u003c/em\u003e. 2017;101(2):430-436. doi:10.1097/TP.0000000000001129\u003c/li\u003e\n\u003cli\u003eVanhove T, Vermeulen T, Annaert P, Lerut E, Kuypers DRJ. High Intrapatient Variability of Tacrolimus Concentrations Predicts Accelerated Progression of Chronic Histologic Lesions in Renal Recipients. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2016;16(10):2954-2963. doi:10.1111/ajt.13803\u003c/li\u003e\n\u003cli\u003ePiburn KH, Sigurjonsdottir VK, Indridason OS, et al. Patterns in Tacrolimus Variability and Association with De Novo Donor-Specific Antibody Formation in Pediatric Kidney Transplant Recipients. \u003cem\u003eClinical Journal of the American Society of Nephrology\u003c/em\u003e. 2022;17(8):1194-1203. doi:10.2215/CJN.16421221\u003c/li\u003e\n\u003cli\u003eDuncan-Park S, Dunphy C, Becker J, et al. Remote intervention engagement and outcomes in the Clinical Trials in Organ Transplantation in Children consortium multisite trial. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2021;21(9):3112-3122. doi:10.1111/ajt.16567\u003c/li\u003e\n\u003cli\u003eShemesh E, Bucuvalas JC, Anand R, et al. The Medication Level Variability Index (MLVI) Predicts Poor Liver Transplant Outcomes: A Prospective Multi-Site Study. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2017;17(10):2668-2678. doi:10.1111/ajt.14276\u003c/li\u003e\n\u003cli\u003eNeuberger JM, Bechstein WO, Kuypers DRJ, et al. Practical Recommendations for Long-term Management of Modifiable Risks in Kidney and Liver Transplant Recipients. \u003cem\u003eTransplantation\u003c/em\u003e. 2017;101(4S):S1-S56. doi:10.1097/TP.0000000000001651\u003c/li\u003e\n\u003cli\u003eHolmberg C. Nonadherence after pediatric renal transplantation. \u003cem\u003eCurr Opin Pediatr\u003c/em\u003e. 2019;31(2):219-225. doi:10.1097/MOP.0000000000000734\u003c/li\u003e\n\u003cli\u003eHsiau M, Fernandez HE, Gjertson D, Ettenger RB, Tsai EW. Monitoring Nonadherence and Acute Rejection With Variation in Blood Immunosuppressant Levels in Pediatric Renal Transplantation. \u003cem\u003eTransplantation\u003c/em\u003e. 2011;92(8):918-922. doi:10.1097/TP.0b013e31822dc34f\u003c/li\u003e\n\u003cli\u003eTaber DJ, Hirsch J, Keys A, Su Z, McGillicuddy JW. Etiologies and Outcomes Associated With Tacrolimus Levels Out of a Typical Range That Lead to High Variability in Kidney Transplant Recipients. \u003cem\u003eTher Drug Monit\u003c/em\u003e. 2021;43(3):401-407. doi:10.1097/FTD.0000000000000863\u003c/li\u003e\n\u003cli\u003eGold A, T\u0026ouml;nshoff B, D\u0026ouml;hler B, S\u0026uuml;sal C. Association of graft survival with tacrolimus exposure and late intra‐patient tacrolimus variability in pediatric and young adult renal transplant recipients\u0026mdash;an international CTS registry analysis. \u003cem\u003eTransplant International\u003c/em\u003e. 2020;33(12):1681-1692. doi:10.1111/tri.13726\u003c/li\u003e\n\u003cli\u003eS\u0026uuml;sal C, D\u0026ouml;hler B. Late intra-patient tacrolimus trough level variability as a major problem in kidney transplantation: A Collaborative Transplant Study Report. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2019;19(10):2805-2813. doi:10.1111/ajt.15346\u003c/li\u003e\n\u003cli\u003eShah PB, Ennis JL, Cunningham PN, Josephson MA, McGill RL. The Epidemiologic Burden of Tacrolimus Variability among Kidney Transplant Recipients in the United States. \u003cem\u003eAm J Nephrol\u003c/em\u003e. 2019;50(5):370-374. doi:10.1159/000503167\u003c/li\u003e\n\u003cli\u003eKuypers DRJ. Intrapatient Variability of Tacrolimus Exposure in Solid Organ Transplantation: A Novel Marker for Clinical Outcome. \u003cem\u003eClin Pharmacol Ther\u003c/em\u003e. 2020;107(2):347-358. doi:10.1002/cpt.1618\u003c/li\u003e\n\u003cli\u003eHerblum J, Dacouris N, Huang M, et al. Retrospective Analysis of Tacrolimus Intrapatient Variability as a Measure of Medication Adherence. \u003cem\u003eCan J Kidney Health Dis\u003c/em\u003e. 2021;8:205435812110217. doi:10.1177/20543581211021742\u003c/li\u003e\n\u003cli\u003eMcGillicuddy JW, Chandler JL, Sox LR, Taber DJ. Exploratory Analysis of the Impact of an mHealth Medication Adherence Intervention on Tacrolimus Trough Concentration Variability: Post Hoc Results of a Randomized Controlled Trial. \u003cem\u003eAnnals of Pharmacotherapy\u003c/em\u003e. 2020;54(12):1185-1193. doi:10.1177/1060028020931806\u003c/li\u003e\n\u003cli\u003eGokoel SRM, Zwart TC, Moes DJAR, van der Boog PJM, de Fijter JW. No Apparent Influence of Nonadherence on Tacrolimus Intrapatient Variability in Stable Kidney Transplant Recipients. \u003cem\u003eTher Drug Monit\u003c/em\u003e. 2020;42(5):702-709. doi:10.1097/FTD.0000000000000772\u003c/li\u003e\n\u003cli\u003eKo H, Kim HK, Chung C, et al. Association between medication adherence and intrapatient variability in tacrolimus concentration among stable kidney transplant recipients. \u003cem\u003eSci Rep\u003c/em\u003e. 2021;11(1):5397. doi:10.1038/s41598-021-84868-5\u003c/li\u003e\n\u003cli\u003eSch\u0026auml;fer-Keller P, Steiger J, Bock A, Denhaerynck K, De Geest S. Diagnostic Accuracy of Measurement Methods to Assess Non-Adherence to Immunosuppressive Drugs in Kidney Transplant Recipients. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2008;8(3):616-626. doi:10.1111/j.1600-6143.2007.02127.x\u003c/li\u003e\n\u003cli\u003eDuncan S, Annunziato RA, Dunphy C, LaPointe Rudow D, Shneider BL, Shemesh E. A systematic review of immunosuppressant adherence interventions in transplant recipients: Decoding the streetlight effect. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2018;22(1). doi:10.1111/petr.13086\u003c/li\u003e\n\u003cli\u003eOsterberg L, Blaschke T. Adherence to Medication. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e. 2005;353(5):487-497. doi:10.1056/NEJMra050100\u003c/li\u003e\n\u003cli\u003eButler JA, Peveler RC, Roderick P, Horne R, Mason JC. Measuring compliance with drug regimens after renal transplantation: comparison of self-report and clinician rating with electronic monitoring. \u003cem\u003eTransplantation\u003c/em\u003e. 2004;77(5):786-789. doi:10.1097/01.TP.0000110412.20050.36\u003c/li\u003e\n\u003cli\u003eDe Geest S, Vanhaecke J. Methodological issues in transplant compliance research. \u003cem\u003eTransplant Proc\u003c/em\u003e. 1999;31(4):81S-83S. doi:10.1016/S0041-1345(99)00137-2\u003c/li\u003e\n\u003cli\u003eDenhaerynck K, Dobbels F, Ko\u0026scaron;ť\u0026aacute;lov\u0026aacute; B, De Geest S. Psychometric Properties of the BAASIS: A Meta-analysis of Individual Participant Data. \u003cem\u003eTransplantation\u003c/em\u003e. 2023;107(8):1795-1809. doi:10.1097/TP.0000000000004574\u003c/li\u003e\n\u003cli\u003eDobbels F, Berben L, De Geest S, et al. The Psychometric Properties and Practicability of Self-Report Instruments to Identify Medication Nonadherence in Adult Transplant Patients: A Systematic Review. \u003cem\u003eTransplantation\u003c/em\u003e. 2010;90(2):205-219. doi:10.1097/TP.0b013e3181e346cd\u003c/li\u003e\n\u003cli\u003eMarsicano E de O, Fernandes N da S, Colugnati F, et al. Transcultural adaptation and initial validation of Brazilian-Portuguese version of the Basel assessment of adherence to immunosuppressive medications scale (BAASIS) in kidney transplants. \u003cem\u003eBMC Nephrol\u003c/em\u003e. 2013;14(1):108. doi:10.1186/1471-2369-14-108\u003c/li\u003e\n\u003cli\u003eRodrigo E, Segundo DS, Fern\u0026aacute;ndez-Fresnedo G, et al. Within-Patient Variability in Tacrolimus Blood Levels Predicts Kidney Graft Loss and Donor-Specific Antibody Development. \u003cem\u003eTransplantation\u003c/em\u003e. 2016;100(11):2479-2485. doi:10.1097/TP.0000000000001040\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Regan JA, Canney M, Connaughton DM, et al. Tacrolimus trough-level variability predicts long-term allograft survival following kidney transplantation. \u003cem\u003eJ Nephrol\u003c/em\u003e. 2016;29(2):269-276. doi:10.1007/s40620-015-0230-0\u003c/li\u003e\n\u003cli\u003ePrytula AA, Bouts AH, Mathot RAA, et al. Intra‐patient variability in tacrolimus trough concentrations and renal function decline in pediatric renal transplant recipients. \u003cem\u003ePediatr Transplant\u003c/em\u003e. 2012;16(6):613-618. doi:10.1111/j.1399-3046.2012.01727.x\u003c/li\u003e\n\u003cli\u003eBorra LCP, Roodnat JI, Kal JA, Mathot RAA, Weimar W, van Gelder T. High within-patient variability in the clearance of tacrolimus is a risk factor for poor long-term outcome after kidney transplantation. \u003cem\u003eNephrology Dialysis Transplantation\u003c/em\u003e. 2010;25(8):2757-2763. doi:10.1093/ndt/gfq096\u003c/li\u003e\n\u003cli\u003eHooper DK, Varnell CD, Rich K, et al. A Medication Adherence Promotion System to Reduce Late Kidney Allograft Rejection: A Quality Improvement Study. \u003cem\u003eAmerican Journal of Kidney Diseases\u003c/em\u003e. 2022;79(3):335-346. doi:10.1053/j.ajkd.2021.06.021\u003c/li\u003e\n\u003cli\u003eLeino AD, King EC, Jiang W, et al. Assessment of tacrolimus intrapatient variability in stable adherent transplant recipients: Establishing baseline values. \u003cem\u003eAmerican Journal of Transplantation\u003c/em\u003e. 2019;19(5):1410-1420. doi:10.1111/ajt.15199\u003c/li\u003e\n\u003cli\u003eVan Arendonk KJ, James NT, Boyarsky BJ, et al. Age at Graft Loss after Pediatric Kidney Transplantation. \u003cem\u003eClinical Journal of the American Society of Nephrology\u003c/em\u003e. 2013;8(6):1019-1026. doi:10.2215/CJN.10311012\u003c/li\u003e\n\u003cli\u003eLieb M, Hepp T, Schiffer M, Opgenoorth M, Erim Y. Accuracy and concordance of measurement methods to assess non-adherence after renal transplantation - a prospective study. \u003cem\u003eBMC Nephrol\u003c/em\u003e. 2020;21(1):114. doi:10.1186/s12882-020-01781-1\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1.\u0026nbsp;\u003c/strong\u003eCharacteristics of pediatric transplant recipients at adherence assessment\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at enrollment (years),\u0026nbsp;\u003c/strong\u003emedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e17 [12,19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (32)\u003c/p\u003e\n \u003cp\u003e51 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, Ethnicity,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eBlack, Non-Hispanic\u003c/p\u003e\n \u003cp\u003eBlack, Hispanic\u003c/p\u003e\n \u003cp\u003eWhite, Non-Hispanic\u003c/p\u003e\n \u003cp\u003eWhite, Hispanic\u003c/p\u003e\n \u003cp\u003eAsian, Non-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27 (36)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003cp\u003e38 (51)\u003c/p\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance type,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (52)\u003c/p\u003e\n \u003cp\u003e18 (24)\u003c/p\u003e\n \u003cp\u003e18 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause of ESKD,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eCongenital/Inherited\u003c/p\u003e\n \u003cp\u003eGlomerular\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (48)\u003c/p\u003e\n \u003cp\u003e32 (43)\u003c/p\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor status,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eLiving Donor\u003c/p\u003e\n \u003cp\u003eDeceased Donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (37)\u003c/p\u003e\n \u003cp\u003e47 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA mismatch,\u0026nbsp;\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eHLA match 0-1\u003c/p\u003e\n \u003cp\u003eHLA match 2-3\u003c/p\u003e\n \u003cp\u003eHLA match 4-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (51)\u003c/p\u003e\n \u003cp\u003e28 (37)\u003c/p\u003e\n \u003cp\u003e9 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83.6207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of tacrolimus levels per patient,\u0026nbsp;\u003c/strong\u003emedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3793%;\"\u003e\n \u003cp\u003e9 [6,13]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eESKD: End-stage kidney disease; HLA: human leukocyte antigen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u003c/strong\u003e. Comparison of adherent and nonadherent patients, and patients with IPV \u0026lt;30% and \u0026ge;30% (n=75)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"881\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdherent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonadherent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPV \u0026lt; 30%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPV \u0026ge; 30%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e38 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e37 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e50 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e25 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at enrollment (years)\u003c/strong\u003e, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e17 [12,19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e16 [13,20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e17 [12,19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e16 [13,20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (26)\u003c/p\u003e\n \u003cp\u003e28 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (38)\u003c/p\u003e\n \u003cp\u003e23 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (28)\u003c/p\u003e\n \u003cp\u003e36 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (40)\u003c/p\u003e\n \u003cp\u003e15 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, Ethnicity\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003cp\u003eBlack, Non-Hispanic, n (%)\u003c/p\u003e\n \u003cp\u003eBlack, Hispanic, n (%)\u003c/p\u003e\n \u003cp\u003eWhite, Non-Hispanic, n (%)\u003c/p\u003e\n \u003cp\u003eWhite, Hispanic, n (%)\u003c/p\u003e\n \u003cp\u003eAsian, Non-Hispanic, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (26)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e4 (11)\u003c/p\u003e\n \u003cp\u003e22 (58)\u003c/p\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (46)\u003c/p\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003cp\u003e3 (8)\u003c/p\u003e\n \u003cp\u003e16 (43)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (32)\u003c/p\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003cp\u003e28 (56)\u003c/p\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (44)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e3 (12)\u003c/p\u003e\n \u003cp\u003e10 (40)\u003c/p\u003e\n \u003cp\u003e1 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime since transplant (years)\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e3 [1,5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e4 [2,6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e3 [2,5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e3 [2,5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance type\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003cp\u003eMedicaid, n (%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMedicare, n (%)\u003c/p\u003e\n \u003cp\u003ePrivate Insurance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (42)\u003c/p\u003e\n \u003cp\u003e12 (32)\u003c/p\u003e\n \u003cp\u003e10 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (62)\u003c/p\u003e\n \u003cp\u003e6 (16)\u003c/p\u003e\n \u003cp\u003e8 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (46)\u003c/p\u003e\n \u003cp\u003e12 (24)\u003c/p\u003e\n \u003cp\u003e15 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (64)\u003c/p\u003e\n \u003cp\u003e6 (24)\u003c/p\u003e\n \u003cp\u003e3 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of TCMR\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e17 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e7 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e13 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of ABMR\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e5 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e5 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteinuria\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e3 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e6 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e5 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of \u003cem\u003edn\u003c/em\u003eDSA Class II\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e16 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e25 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e23 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e19 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPV (%)\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e20 [15,26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e31 [23,43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e20 [15,24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e39 [33,44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.706%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAS\u003c/strong\u003e, median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2157%;\"\u003e\n \u003cp\u003e0 [0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9398%;\"\u003e\n \u003cp\u003e2 [1,4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e0 [0,1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8967%;\"\u003e\n \u003cp\u003e2 [1,4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.17253%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIPV: intrapatient variability; TCMR: T-cell mediated rejection; ABMR: antibody-mediated rejection; proteinuria: urine protein/creatinine ratio above 0.5 mg/mg that persisted over 3 months; \u003cem\u003edn\u003c/em\u003eDSA: \u003cem\u003ede novo\u003c/em\u003e donor-specific antibodies; CAS: composite adherence score\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"kidney transplant, pediatric, adherence, tacrolimus variability","lastPublishedDoi":"10.21203/rs.3.rs-5334772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5334772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Long-term survival of kidney allografts is limited by multiple factors, including nonadherence. High intrapatient variability (IPV) in tacrolimus levels (≥30%) is associated with \u003cem\u003ede novo\u003c/em\u003e donor-specific antibody (\u003cem\u003edn\u003c/em\u003eDSA) formation,\u0026nbsp;increased risk of rejection and graft loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We prospectively analyzed the association between tacrolimus IPV and nonadherence in pediatric kidney transplant recipients. We derived a composite adherence score from 0-3 points based on (1) Basel Assessment of Adherence to Immunosuppressive Medical Scale\u003csup\u003eÓ\u003c/sup\u003e; (2) healthcare team score; and (3) intentionally missed laboratory or clinic visits. A score of 1 or more was considered nonadherent. Tacrolimus 12-hour trough levels, patient characteristics and clinical outcomes were collected. Tacrolimus IPV was calculated as the coefficient of variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The nonadherent group had a significantly higher median tacrolimus IPV (31%) as compared to the adherent cohort (20%) (p \u0026lt; 0.001), with a positive correlation between tacrolimus IPV and composite adherence score (r = 0.44, p \u0026lt; 0.001). Antibody and T-cell mediated rejection, along with \u003cem\u003edn\u003c/em\u003eDSA formation were more prevalent in nonadherent patients and those with a high tacrolimus IPV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Tacrolimus IPV may be a useful biomarker to identify high-risk patients, allowing for early interventions to prevent adverse graft outcomes.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Intrapatient Tacrolimus Variability is Associated with Medical Nonadherence among Pediatric Kidney Transplant Recipients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 16:07:14","doi":"10.21203/rs.3.rs-5334772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b9aed51-bd4b-47dd-8dce-935adfe4ee33","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-19T19:23:02+00:00","versionOfRecord":{"articleIdentity":"rs-5334772","link":"https://doi.org/10.3389/frtra.2025.1572928","journal":{"identity":"frontiers-in-transplantation","isVorOnly":true,"title":"Frontiers in Transplantation"},"publishedOn":"2025-03-16 00:00:00","publishedOnDateReadable":"March 16th, 2025"},"versionCreatedAt":"2024-11-04 16:07:14","video":"","vorDoi":"10.3389/frtra.2025.1572928","vorDoiUrl":"https://doi.org/10.3389/frtra.2025.1572928","workflowStages":[]},"version":"v1","identity":"rs-5334772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5334772","identity":"rs-5334772","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}